Visual Prompt Tuning On Vtab 1K Specialized 4
평가 지표
Mean Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Mean Accuracy | Paper Title | Repository |
|---|---|---|---|
| VPT-Deep(ViT-B/16_MAE_pretrained_ImageNet-1K) | 60.61 | Visual Prompt Tuning | |
| SPT-Shallow(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 83.93 | Revisiting the Power of Prompt for Visual Tuning | - |
| SPT-Deep(ViT-B/16_MAE_pretrained_ImageNet-1K) | 83.15 | Revisiting the Power of Prompt for Visual Tuning | - |
| SPT-Deep(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 84.95 | Revisiting the Power of Prompt for Visual Tuning | - |
| VPT-Shallow(ViT-B/16_MAE_pretrained_ImageNet-1K) | 69.65 | Visual Prompt Tuning | |
| GateVPT(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 83.38 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | |
| GateVPT(ViT-B/16_MAE_pretrained_ImageNet-1K) | 76.86 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | |
| VPT-Deep(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 83.04 | Visual Prompt Tuning | |
| VPT-Shallow(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 82.26 | Visual Prompt Tuning | |
| SPT-Shallow(ViT-B/16_MAE_pretrained_ImageNet-1K) | 80.90 | Revisiting the Power of Prompt for Visual Tuning | - |
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